Tracking Predictive ADME/Tox Advances

Innovative Cell Imaging, Assays, Animal Models, and Software Solutions Are Having an Impact!--h2>

John Russell

Cell imaging is an increasingly useful tool in predictive ADME/Tox strategies, according to Karen Tilmant, Ph.D., research scientist, investigative nonclinical safety, at UCB Pharma, who spoke at Mondial Research Group’s “Conference on Predictive Human Toxicity and ADME/Tox Studies” held recently in Brussels.

“One test that is gaining importance is the automated micronucleus test, based on cell imaging of fluorescent-labeled nuclei and micronuclei,” said Dr. Tilmant. The formation of micronuclei is a result of chromosome loss (aneugens) or detachment of a chromosome fragment (clastogens).

“The manually performed micronucleus test is validated and widely used, but it is low throughput. Also, counting can vary with the person who evaluates the microscope slides. The automated micronucleus test has advantages of being medium throughput, providing objective reading, and it can easily be applied in screening.”

The goal of predictive ADME/Tox, of course, is to reduce late-stage attrition. Identifying genotoxicity early is especially critical, noted Dr. Tilmant, “because it is often an insurmountable hurdle in drug development. Even a small mutation can lead to cancer.”

UCB’s in vitro toxicology group screens compounds with various automated imaging tests “to rank compounds from the same family and to allow the selection of the best compounds for further testing.” Improved cell imaging and high-content screening techniques received a fair amount of discussion at the conference, no doubt because of expanding applications. “Besides genotoxicity tests, cell imaging allows work on different other toxicity markers, through different immunomarkers and fluorescent dyes,” Dr. Tilmant said.

Brain Absorption Assay Advance

Estimating the unbound fraction of a compound in the brain is a key parameter when developing drugs for the central nervous system. Currently, dialysis systems and brain slice assays are the methods of choice. Hinnerk Boriss, Ph.D., CEO of Sovicell, described the company’s two-year-old Transil technology, which, he said, is faster and cheaper than other methods and has been shown to be as accurate as dialysis in work recently published by GlaxoSmithKline.

“The old way of looking at how difficult it is for a drug to pass the blood brain barrier (BBB) is actually the wrong question. Every drug gets into the brain, just to different extents. The right question is what happens when the drug actually gets into brain,” said Dr. Boriss. He noted that many drugs have drastically differing BBB penetration profiles—e.g., Sumatriptan (poor BBB penetrator) and Sertraline (high penetrator)—but are also effective in the brain.

Transil uses porous silica beads as a carrier for biological molecules such as membranes and proteins. Sovicell’s Transil Brain Absorption kit is made with reconstituted porcine brain lipid membranes immobilized on the beads.

After mixing and incubation for two minutes the beads are separated by low-speed centrifugation.

“If you do LC/MS, which is the standard method in this field, then you need longer gradients to interpolate the biological matrix from the compound of interest to quantify them. With our method there is no need to separate the biological matrix because that’s the beads. We just remove the beads and you can straight away analyze and quantify the samples. It’s three to four times faster for analyzing and quantifying the results.”

Dr. Boriss emphasized that lipid binding is a more important indicator than protein binding “because there are very few proteins that contribute to binding. It’s about 28 micromolar albumin that contributes to binding versus about 125 micromolar lipid and the affinity of drugs to lipids is three to four orders of magnitude higher on average—higher than binding to albumin.”

Sovicell offers assay chips and assay services on a small scale. Dr. Boriss believes the technology has the potential to enable companies to ramp up screening activities, reduce investments in instruments, or redirect other activities to instruments previously tied up with processing samples from dialysis.

Humanized Mouse Tox Model

Current animal models are often poorly predictive of ADMET in man, noted Mike Piper, Ph.D., senior business development manager at CXR Biosciences. “This is driven by profound interspecies differences in the expression levels and functions of proteins involved in ADMET.” It’s also a major reason for development failure in the pharmaceutical and chemical industries.

CXR and TaconicArtemis have developed transADMET mice panels in which key murine ADMET genes are knocked out and their human counterparts inserted. Dr. Piper discussed three such panels—Cytochrome P450, Nuclear Receptor, and Drug Transporter—each representing critical pathways in compound metabolism, disposition, and safety.

For example, Cytochrome P450-dependent monooxygenases are a group of enzymes that account for the Phase I metabolism of the majority of drugs. Together, CYP3A4 and CYP2D6 catalyze the metabolism of over 60% of drugs in clinical use, according to Dr. Piper who noted these enzymes have diverged significantly between species, both in their multiplicity and substrate specificity. This divergence can cause altered drug metabolism profiles between animals and humans, leading to differences in pharmacokinetics, efficacy, and toxicity.

“We have derived and sourced a series of humanized and knockout CYP3A4 and CYP2D6 mouse models,” said Dr. Piper. “Depending on your needs, we can offer transADMET models where CYP450 expression is under control of the human promoter, or models with gut- or liver-specific CYP3A4 expression.”

The situation is much the same for nuclear receptor panels. Nuclear hormone receptors play a major role in regulating the body’s response to chemical exposure. The Pregnane X receptor (PXR) and Constitutive androstane receptor (CAR) have the ability to bind a wide range of exogenous and endogenous ligands and to control the expression of genes highly relevant to compound metabolism such as the cytochrome P450s and drug transporters.

However, sequence variation in PXR and CAR between animals and humans results in differences in the ability of exogenous ligands to interact and activate these transcription factors. Experimental data obtained in traditional animal models or in vitro test systems, may therefore, not reflect the interactions that occur in man, noted Dr. Piper.

Rule-Based Predictive Modeling

Functional genomics has yet to deliver on its promise for predictive ADME/Tox said Quin Wills, M.D., CSO at SimuGen. This is largely because of the difficulty researchers have adequately interpreting results, especially multivariate results, he contended.

“The problem isn’t the data, it’s the models.” Physicists have remarkably accurate models constructed from first principles but biologists don’t. What’s needed, said Dr. Wills, are flexible modeling platforms that can incorporate prior knowledge (learned biology) by using Bayesian statistical techniques and also incorporate rules (e.g., weights and thresholds) created by researchers.

SimuGen offers high-throughput screening analysis software and HT Stream, as well as various services. One of HT Stream’s strengths, said Dr. Wills, is its ability to easily create rule-based models for use when analyzing assay results.

Suppose you are screening compounds and looking for late-stage apoptosis by identifying Caspase overexpression and nuclear morphology. “You could create a rule that says when Caspase9 is expressed 2.5 times normal and when there is 25 percent nuclear shrinkage, call late-stage apoptosis. Then the software takes over and uses that rule and tries to find the lowest dosage at which the rule starts becoming true.”

A common criticism of modeling software is that it’s difficult to use for non-mathematicians. Dr. Wills agrees but he insists that “the HT Stream has been structured in a way that anybody can use it. It’s a very simple workflow.”

“The only restriction is that for every chemical you test, there must be at least six concentrations done in triplicate, so at least 18 data points. If you are measuring the level of expression of a gene, you need 18 data points to allow us to do rigorous automated quality control. Once you start working with less data it’s very difficult to do that automatically.”

Global vs. Local QSAR

Johann Gasteiger, Ph.D., founder of Molecular Networks, presented an overview of various computational approaches to predictive ADME/Tox based on the company’s extensive cheminformatics suite.

In discussing modeling chemical toxicity, he noted that QSAR (quantitative structure activity relationship) efforts in toxicology tend to focus on a class of compounds. However, compounds in the same class often have very different toxic modes of action. For example, he presented data on phenols indicating a diversity of mechanism of action including polar narcotics, uncouplers of oxidative phosphorylation, precursors to soft electrophiles, and soft electrophiles themselves.

For this reason, he said, using global QSAR models is a poor idea. It’s necessary to first classify chemicals according to their toxic mechanism of action and then derive local QSAR models. Doing this greatly enhances the predictability of the models.